{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "58b340ff-3de8-477f-be30-266cb7efe8b7",
   "metadata": {},
   "source": [
    "# 词向量和句向量的降维可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b7357ec3-ce36-4d3d-9cc5-bf76d160d943",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-15T06:49:07.409327Z",
     "iopub.status.busy": "2024-07-15T06:49:07.409132Z",
     "iopub.status.idle": "2024-07-15T06:49:08.119689Z",
     "shell.execute_reply": "2024-07-15T06:49:08.119210Z",
     "shell.execute_reply.started": "2024-07-15T06:49:07.409305Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: paddlenlp\n",
      "Version: 2.5.0\n",
      "Summary: Easy-to-use and powerful NLP library with Awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications, including Neural Search, Question Answering, Information Extraction and Sentiment Analysis end-to-end system.\n",
      "Home-page: https://github.com/PaddlePaddle/PaddleNLP\n",
      "Author: PaddleNLP Team\n",
      "Author-email: paddlenlp@baidu.com\n",
      "License: Apache 2.0\n",
      "Location: /Users/arcment/miniconda3/envs/pnlp/lib/python3.8/site-packages\n",
      "Requires: colorama, colorlog, datasets, dill, fastapi, huggingface-hub, jieba, multiprocess, paddle2onnx, paddlefsl, protobuf, rich, sentencepiece, seqeval, tqdm, typer, uvicorn, visualdl\n",
      "Required-by: \n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip show paddlenlp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b2ab6152-55f8-4f9f-a2a5-e1ed73aa0b59",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-15T06:49:08.120656Z",
     "iopub.status.busy": "2024-07-15T06:49:08.120495Z",
     "iopub.status.idle": "2024-07-15T06:49:08.752862Z",
     "shell.execute_reply": "2024-07-15T06:49:08.752301Z",
     "shell.execute_reply.started": "2024-07-15T06:49:08.120639Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: visualdl\n",
      "Version: 2.4.2\n",
      "Summary: Visualize Deep Learning\n",
      "Home-page: UNKNOWN\n",
      "Author: PaddlePaddle and Echarts team\n",
      "Author-email: \n",
      "License: Apache License\n",
      "Location: /Users/arcment/miniconda3/envs/pnlp/lib/python3.8/site-packages\n",
      "Requires: bce-python-sdk, flask, Flask-Babel, matplotlib, multiprocess, numpy, packaging, pandas, Pillow, protobuf, requests, six\n",
      "Required-by: paddlenlp\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip show visualdl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2a28a59-1976-4d72-a41c-57e596d24971",
   "metadata": {},
   "source": [
    "PaddleNLP已预置多个公开的预训练Embedding，可以通过使用`paddlenlp.embeddings.TokenEmbedding`接口加载各种预训练Embedding。我们将计算词与词之间的语义距离，并结合词袋模型获取句子的语义表示。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "843deffb-81df-4fb9-867a-1151b2ef0bb2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-15T06:49:08.754160Z",
     "iopub.status.busy": "2024-07-15T06:49:08.753814Z",
     "iopub.status.idle": "2024-07-15T06:49:10.627875Z",
     "shell.execute_reply": "2024-07-15T06:49:10.627550Z",
     "shell.execute_reply.started": "2024-07-15T06:49:08.754142Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/arcment/miniconda3/envs/pnlp/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "/Users/arcment/miniconda3/envs/pnlp/lib/python3.8/site-packages/_distutils_hack/__init__.py:33: UserWarning: Setuptools is replacing distutils.\n",
      "  warnings.warn(\"Setuptools is replacing distutils.\")\n"
     ]
    }
   ],
   "source": [
    "from paddlenlp.embeddings import TokenEmbedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "10f398d5-fec1-4b3f-bd22-af7b8be22853",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-15T06:49:10.628493Z",
     "iopub.status.busy": "2024-07-15T06:49:10.628340Z",
     "iopub.status.idle": "2024-07-15T06:49:13.119254Z",
     "shell.execute_reply": "2024-07-15T06:49:13.118904Z",
     "shell.execute_reply.started": "2024-07-15T06:49:10.628485Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[2024-07-15 14:49:10,628] [    INFO]\u001b[0m - Loading token embedding...\u001b[0m\n",
      "\u001b[32m[2024-07-15 14:49:13,028] [    INFO]\u001b[0m - Finish loading embedding vector.\u001b[0m\n",
      "\u001b[32m[2024-07-15 14:49:13,030] [    INFO]\u001b[0m - Token Embedding info:             \n",
      "Unknown index: 635963             \n",
      "Unknown token: [UNK]             \n",
      "Padding index: 635964             \n",
      "Padding token: [PAD]             \n",
      "Shape :[635965, 300]\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Object   type: TokenEmbedding(635965, 300, padding_idx=635964, sparse=False)             \n",
       "Unknown index: 635963             \n",
       "Unknown token: [UNK]             \n",
       "Padding index: 635964             \n",
       "Padding token: [PAD]             \n",
       "Parameter containing:\n",
       "Tensor(shape=[635965, 300], dtype=float32, place=Place(cpu), stop_gradient=False,\n",
       "       [[-0.24200200,  0.13931701,  0.07378800, ...,  0.14103900,\n",
       "          0.05592300, -0.08004800],\n",
       "        [-0.08671700,  0.07770800,  0.09515300, ...,  0.11196400,\n",
       "          0.03082200, -0.12893000],\n",
       "        [-0.11436500,  0.12201900,  0.02833000, ...,  0.11068700,\n",
       "          0.03607300, -0.13763499],\n",
       "        ...,\n",
       "        [ 0.02628800, -0.00008300, -0.00393500, ...,  0.00654000,\n",
       "          0.00024600, -0.00662600],\n",
       "        [-0.01635122,  0.00455277, -0.00745473, ...,  0.05697170,\n",
       "         -0.01014163,  0.00078839],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 初始化TokenEmbedding， 预训练embedding未下载时会自动下载并加载数据\n",
    "# 直接载入一个300维中文词向量预训练模型\n",
    "token_embedding = TokenEmbedding(embedding_name=\"w2v.baidu_encyclopedia.target.word-word.dim300\")\n",
    "token_embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a6d654d3-b3a1-4d0e-b1e1-a2405e99e225",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-15T06:49:13.120164Z",
     "iopub.status.busy": "2024-07-15T06:49:13.120078Z",
     "iopub.status.idle": "2024-07-15T06:49:13.124116Z",
     "shell.execute_reply": "2024-07-15T06:49:13.123847Z",
     "shell.execute_reply.started": "2024-07-15T06:49:13.120154Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.260801,  0.1047  ,  0.129453, -0.257317, -0.16152 ,  0.19567 ,\n",
       "        -0.074868,  0.361168,  0.245882, -0.219141, -0.388083,  0.235189,\n",
       "         0.029316,  0.154215, -0.354343,  0.017746,  0.009028,  0.01197 ,\n",
       "        -0.121429,  0.096542,  0.009255,  0.039721,  0.363704, -0.239497,\n",
       "        -0.41168 ,  0.16958 ,  0.261758,  0.022383, -0.053248, -0.000994,\n",
       "        -0.209913, -0.208296,  0.197332, -0.3426  , -0.162112,  0.134557,\n",
       "        -0.250201,  0.431298,  0.303116,  0.517221,  0.243843,  0.022219,\n",
       "        -0.136554, -0.189223,  0.148563, -0.042963, -0.456198,  0.14546 ,\n",
       "        -0.041207,  0.049685,  0.20294 ,  0.147355, -0.206953, -0.302796,\n",
       "        -0.111834,  0.128183,  0.289539, -0.298934, -0.096412,  0.063079,\n",
       "         0.324821, -0.144471,  0.052456,  0.088761, -0.040925, -0.103281,\n",
       "        -0.216065, -0.200878, -0.100664,  0.170614, -0.355546, -0.062115,\n",
       "        -0.52595 , -0.235442,  0.300866, -0.521523, -0.070713, -0.331768,\n",
       "         0.023021,  0.309111, -0.125696,  0.016723, -0.0321  , -0.200611,\n",
       "         0.057294, -0.128891, -0.392886,  0.423002,  0.282569, -0.212836,\n",
       "         0.450132,  0.067604, -0.124928, -0.294086,  0.136479,  0.091505,\n",
       "        -0.061723, -0.577495,  0.293856, -0.401198,  0.302559, -0.467656,\n",
       "         0.021708, -0.088507,  0.088322, -0.015567,  0.136594,  0.112152,\n",
       "         0.005394,  0.133818,  0.071278, -0.198807,  0.043538,  0.116647,\n",
       "        -0.210486, -0.217972, -0.320675,  0.293977,  0.277564,  0.09591 ,\n",
       "        -0.359836,  0.473573,  0.083847,  0.240604,  0.441624,  0.087959,\n",
       "         0.064355, -0.108271,  0.055709,  0.380487, -0.045262,  0.04014 ,\n",
       "        -0.259215, -0.398335,  0.52712 , -0.181298,  0.448978, -0.114245,\n",
       "        -0.028225, -0.146037,  0.347414, -0.076505,  0.461865, -0.105099,\n",
       "         0.131892,  0.079946,  0.32422 , -0.258629,  0.05225 ,  0.566337,\n",
       "         0.348371,  0.124111,  0.229154,  0.075039, -0.139532, -0.08839 ,\n",
       "        -0.026703, -0.222828, -0.106018,  0.324477,  0.128269, -0.045624,\n",
       "         0.071815, -0.135702,  0.261474,  0.297334, -0.031481,  0.18959 ,\n",
       "         0.128716,  0.090022,  0.037609, -0.049669,  0.092909,  0.0564  ,\n",
       "        -0.347994, -0.367187, -0.292187,  0.021649, -0.102004, -0.398568,\n",
       "        -0.278248, -0.082361, -0.161823,  0.044846,  0.212597, -0.013164,\n",
       "         0.005527, -0.004024,  0.176243,  0.237274, -0.174856, -0.197214,\n",
       "         0.150825, -0.164427, -0.244255, -0.14897 ,  0.098907, -0.295891,\n",
       "        -0.013408, -0.146875, -0.126049,  0.033235, -0.133444, -0.003258,\n",
       "         0.082053, -0.162569,  0.283657,  0.315608, -0.171281, -0.276051,\n",
       "         0.258458,  0.214045, -0.129798, -0.511728,  0.198481, -0.35632 ,\n",
       "        -0.186253, -0.203719,  0.22004 , -0.016474,  0.080321, -0.463004,\n",
       "         0.290794, -0.003445,  0.061247, -0.069157, -0.022525,  0.13514 ,\n",
       "         0.001354,  0.011079,  0.014223, -0.079145, -0.41402 , -0.404242,\n",
       "        -0.301509,  0.036712,  0.037076, -0.061683, -0.202429,  0.130216,\n",
       "         0.054355,  0.140883, -0.030627, -0.281293, -0.28059 , -0.214048,\n",
       "        -0.467033,  0.203632, -0.541544,  0.183898, -0.129535, -0.286422,\n",
       "        -0.162222,  0.262487,  0.450505,  0.11551 , -0.247965, -0.15837 ,\n",
       "         0.060613, -0.285358,  0.498203,  0.025008, -0.256397,  0.207582,\n",
       "         0.166383,  0.669677, -0.067961, -0.049835, -0.444369,  0.369306,\n",
       "         0.134493, -0.080478, -0.304565, -0.091756,  0.053657,  0.114497,\n",
       "        -0.076645, -0.123933,  0.168645,  0.018987, -0.260592, -0.019668,\n",
       "        -0.063312, -0.094939,  0.657352,  0.247547, -0.161621,  0.289043,\n",
       "        -0.284084,  0.205076,  0.059885,  0.055871,  0.159309,  0.062181,\n",
       "         0.123634,  0.282932,  0.140399, -0.076253, -0.087103,  0.07262 ]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "token_embedding.search(\"中国\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7b1cfac0-97fd-484f-adec-be5e96b68136",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-15T06:49:13.125568Z",
     "iopub.status.busy": "2024-07-15T06:49:13.125489Z",
     "iopub.status.idle": "2024-07-15T06:49:13.128105Z",
     "shell.execute_reply": "2024-07-15T06:49:13.127888Z",
     "shell.execute_reply.started": "2024-07-15T06:49:13.125559Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7017183, 0.19189896)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score1 = token_embedding.cosine_sim(\"女孩\", \"女人\")\n",
    "score2 = token_embedding.cosine_sim(\"女孩\", \"书籍\")\n",
    "score1, score2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "387902b2-5740-4f26-9c61-a3d9d04bc724",
   "metadata": {},
   "source": [
    "使用余弦相似度衡量词向量与词向量之间的距离。通过实验可以看出“女孩”词向量和“女人”词向量靠得更近，和“书籍”词向量靠得更远。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11a15919-3692-4087-8f6c-9c382117459c",
   "metadata": {},
   "source": [
    "## 使用VisualDL进行词向量的可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "83188f41-f151-4c1e-8e47-1f1784ebec7f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-15T06:49:13.128547Z",
     "iopub.status.busy": "2024-07-15T06:49:13.128458Z",
     "iopub.status.idle": "2024-07-15T06:49:13.315404Z",
     "shell.execute_reply": "2024-07-15T06:49:13.315034Z",
     "shell.execute_reply.started": "2024-07-15T06:49:13.128540Z"
    }
   },
   "outputs": [],
   "source": [
    "# 引入VisualDL的LogWriter记录日志\n",
    "from visualdl import LogWriter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "67a5aa8e-ed18-4d81-aded-1ad5130fad9f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-15T06:49:13.316146Z",
     "iopub.status.busy": "2024-07-15T06:49:13.315998Z",
     "iopub.status.idle": "2024-07-15T06:49:13.467318Z",
     "shell.execute_reply": "2024-07-15T06:49:13.466999Z",
     "shell.execute_reply.started": "2024-07-15T06:49:13.316137Z"
    }
   },
   "outputs": [],
   "source": [
    "# 获取词表中前500个单词\n",
    "labels = token_embedding.vocab.to_tokens(list(range(0, 500)))\n",
    "\n",
    "# 取出单词对应的Embedding\n",
    "test_token_embedding = token_embedding.search(labels)\n",
    "\n",
    "with LogWriter(logdir='./token_hidi') as writer:\n",
    "    writer.add_embeddings(tag='test', mat=[i for i in test_token_embedding], metadata=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "11750ed9-ee3b-4c04-9898-542c2e020158",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-15T06:49:13.467807Z",
     "iopub.status.busy": "2024-07-15T06:49:13.467728Z",
     "iopub.status.idle": "2024-07-15T06:52:39.212625Z",
     "shell.execute_reply": "2024-07-15T06:52:39.211716Z",
     "shell.execute_reply.started": "2024-07-15T06:49:13.467800Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/arcment/Library/Python/3.9/lib/python/site-packages/pandas/core/arrays/masked.py:59: UserWarning: Pandas requires version '1.3.2' or newer of 'bottleneck' (version '1.2.0' currently installed).\n",
      "  from pandas.core import (\n",
      "\u001b[1;33mVisualDL 2.4.1\u001b[0m\n",
      "Running VisualDL at http://0.0.0.0:8050/ (Press CTRL+C to quit)\n",
      "^C\n"
     ]
    }
   ],
   "source": [
    "# 启动VisualDL\n",
    "!visualdl --logdir token_hidi --host 0.0.0.0 --port 8050"
   ]
  },
  {
   "attachments": {
    "0d44c94b-b6f6-4b05-b12b-6170d3131daa.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "4b9e88a1-8022-49fc-b04e-36e9d1404c33",
   "metadata": {},
   "source": [
    "![image.png](attachment:0d44c94b-b6f6-4b05-b12b-6170d3131daa.png)\n",
    "\n",
    "效果如图所示，可以尝试不同的算法、维度、超参数。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94f6cf79-b442-46d7-8233-3f17c740715e",
   "metadata": {},
   "source": [
    "## 使用VisualDL进行句子向量的可视化\n",
    "\n",
    "在许多实际应用场景（如文档检索系统）中， 需要衡量两个句子的语义相似程度。此时我们可以使用词袋模型（Bag of Words，简称BoW）计算句子的语义向量。\n",
    "\n",
    "**首先**，将两个句子分别进行切词，并在TokenEmbedding中查找相应的单词词向量（word embdding）。\n",
    "\n",
    "**然后**，根据词袋模型，将句子的word embedding叠加作为句子向量（sentence embedding）。\n",
    "\n",
    "**最后**，计算两个句子向量的余弦相似度。\n",
    "\n",
    "### 基于TokenEmbedding的词袋模型\n",
    "\n",
    "\n",
    "使用`BoWEncoder`搭建一个BoW模型用于计算句子语义。\n",
    "\n",
    "* `paddlenlp.TokenEmbedding`组建word-embedding层\n",
    "* `paddlenlp.seq2vec.BoWEncoder`组建句子建模层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "16b65a1f-0057-4cac-b3bb-713aef38a4e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-15T06:55:16.740952Z",
     "iopub.status.busy": "2024-07-15T06:55:16.740706Z",
     "iopub.status.idle": "2024-07-15T06:55:16.744998Z",
     "shell.execute_reply": "2024-07-15T06:55:16.744703Z",
     "shell.execute_reply.started": "2024-07-15T06:55:16.740938Z"
    }
   },
   "outputs": [],
   "source": [
    "import paddle\n",
    "import paddle.nn as nn\n",
    "import paddlenlp\n",
    "\n",
    "\n",
    "class BoWModel(nn.Layer):\n",
    "    def __init__(self, embedder):\n",
    "        super().__init__()\n",
    "        self.embedder = embedder\n",
    "        emb_dim = self.embedder.embedding_dim\n",
    "        self.encoder = paddlenlp.seq2vec.BoWEncoder(emb_dim)\n",
    "        self.cos_sim_func = nn.CosineSimilarity(axis=-1)\n",
    "\n",
    "    def get_cos_sim(self, text_a, text_b):\n",
    "        text_a_embedding = self.forward(text_a)\n",
    "        text_b_embedding = self.forward(text_b)\n",
    "        cos_sim = self.cos_sim_func(text_a_embedding, text_b_embedding)\n",
    "        return cos_sim\n",
    "\n",
    "    def forward(self, text):\n",
    "        # Shape: (batch_size, num_tokens, embedding_dim)\n",
    "        embedded_text = self.embedder(text)\n",
    "\n",
    "        # Shape: (batch_size, embedding_dim)\n",
    "        summed = self.encoder(embedded_text)\n",
    "\n",
    "        return summed\n",
    "\n",
    "model = BoWModel(embedder=token_embedding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dee51c38-7cbb-4f80-b786-08167a637d94",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-15T06:57:18.665977Z",
     "iopub.status.busy": "2024-07-15T06:57:18.665331Z",
     "iopub.status.idle": "2024-07-15T06:57:18.679612Z",
     "shell.execute_reply": "2024-07-15T06:57:18.679019Z",
     "shell.execute_reply.started": "2024-07-15T06:57:18.665939Z"
    }
   },
   "outputs": [],
   "source": [
    "import jieba\n",
    "\n",
    "from collections import defaultdict\n",
    "from paddlenlp.data import JiebaTokenizer, Pad, Stack, Tuple, Vocab\n",
    "\n",
    "class Tokenizer(object):\n",
    "    def __init__(self):\n",
    "        self.vocab = {}\n",
    "        self.tokenizer = jieba\n",
    "        self.vocab_path = 'vocab.txt'\n",
    "        self.UNK_TOKEN = '[UNK]'\n",
    "        self.PAD_TOKEN = '[PAD]'\n",
    "\n",
    "    def set_vocab(self, vocab):\n",
    "        self.vocab = vocab\n",
    "        self.tokenizer = JiebaTokenizer(vocab)\n",
    "\n",
    "    def build_vocab(self, sentences):\n",
    "        word_count = defaultdict(lambda: 0)\n",
    "        for text in sentences:\n",
    "            words = jieba.lcut(text)\n",
    "            for word in words:\n",
    "                word = word.strip()\n",
    "                if word.strip() !='':\n",
    "                    word_count[word] += 1\n",
    "\n",
    "        word_id = 0\n",
    "        for word, num in word_count.items():\n",
    "            if num < 5:\n",
    "                continue\n",
    "            self.vocab[word] = word_id\n",
    "            word_id += 1\n",
    "        \n",
    "        self.vocab[self.UNK_TOKEN] = word_id\n",
    "        self.vocab[self.PAD_TOKEN] = word_id + 1\n",
    "        self.vocab = Vocab.from_dict(self.vocab,\n",
    "            unk_token=self.UNK_TOKEN, pad_token=self.PAD_TOKEN)\n",
    "        # dump vocab to file\n",
    "        self.dump_vocab(self.UNK_TOKEN, self.PAD_TOKEN)\n",
    "        self.tokenizer = JiebaTokenizer(self.vocab)\n",
    "        return self.vocab\n",
    "\n",
    "    def dump_vocab(self, unk_token, pad_token):\n",
    "        with open(self.vocab_path, \"w\", encoding=\"utf8\") as f:\n",
    "            for word in self.vocab._token_to_idx:\n",
    "                f.write(word + \"\\n\")\n",
    "    \n",
    "    def text_to_ids(self, text):\n",
    "        input_ids = []\n",
    "        unk_token_id = self.vocab[self.UNK_TOKEN]\n",
    "        for token in self.tokenizer.cut(text):\n",
    "            token_id = self.vocab.token_to_idx.get(token, unk_token_id)\n",
    "            input_ids.append(token_id)\n",
    "\n",
    "        return input_ids\n",
    "\n",
    "    def convert_example(self, example, is_test=False):\n",
    "        input_ids = self.text_to_ids(example['text'])\n",
    "\n",
    "        if not is_test:\n",
    "            label = np.array(example['label'], dtype=\"int64\")\n",
    "            return input_ids, label\n",
    "        else:\n",
    "            return input_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d88ebde7-97cf-4cda-bc7c-8757b9eb2bcb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "329d225b-64c8-4094-b1a2-be2c25927573",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-15T06:57:39.664923Z",
     "iopub.status.busy": "2024-07-15T06:57:39.663961Z",
     "iopub.status.idle": "2024-07-15T06:57:39.785547Z",
     "shell.execute_reply": "2024-07-15T06:57:39.784976Z",
     "shell.execute_reply.started": "2024-07-15T06:57:39.664861Z"
    }
   },
   "outputs": [],
   "source": [
    "tokenizer = Tokenizer()\n",
    "tokenizer.set_vocab(vocab=token_embedding.vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "26e4bdc0-0b03-4e71-bb0e-09100acdb7d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-15T06:57:40.384614Z",
     "iopub.status.busy": "2024-07-15T06:57:40.383985Z",
     "iopub.status.idle": "2024-07-15T06:57:40.391897Z",
     "shell.execute_reply": "2024-07-15T06:57:40.391285Z",
     "shell.execute_reply.started": "2024-07-15T06:57:40.384574Z"
    }
   },
   "outputs": [],
   "source": [
    "text_pairs = {}\n",
    "with open(\"text_pair.txt\", \"r\", encoding=\"utf8\") as f:\n",
    "    for line in f:\n",
    "        text_a, text_b = line.strip().split(\"\\t\")\n",
    "        if text_a not in text_pairs:\n",
    "            text_pairs[text_a] = []\n",
    "        text_pairs[text_a].append(text_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bd89bb9e-071a-4ab8-915c-4729cb604dee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-15T06:57:40.902070Z",
     "iopub.status.busy": "2024-07-15T06:57:40.901495Z",
     "iopub.status.idle": "2024-07-15T06:57:40.945313Z",
     "shell.execute_reply": "2024-07-15T06:57:40.944861Z",
     "shell.execute_reply.started": "2024-07-15T06:57:40.902040Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "text_a: 多项式矩阵左共轭积对偶Sylvester共轭和数学算子完备参数解\n",
      "text_b: 多项式矩阵的左共轭积及其应用\n",
      "cosine_sim: 0.8861939311027527\n",
      "\n",
      "text_a: 多项式矩阵左共轭积对偶Sylvester共轭和数学算子完备参数解\n",
      "text_b: 退化阻尼对高维可压缩欧拉方程组经典解的影响\n",
      "cosine_sim: 0.7975841760635376\n",
      "\n",
      "text_a: 多项式矩阵左共轭积对偶Sylvester共轭和数学算子完备参数解\n",
      "text_b: Burgers方程基于特征正交分解方法的数值解法研究\n",
      "cosine_sim: 0.818878173828125\n",
      "\n",
      "text_a: 多项式矩阵左共轭积对偶Sylvester共轭和数学算子完备参数解\n",
      "text_b: 有界对称域上解析函数空间的若干性质\n",
      "cosine_sim: 0.8041475415229797\n",
      "\n",
      "text_a: 多项式矩阵左共轭积对偶Sylvester共轭和数学算子完备参数解\n",
      "text_b: 基于卷积神经网络的图像复杂度研究与应用\n",
      "cosine_sim: 0.7444741129875183\n",
      "\n",
      "text_a: 多项式矩阵左共轭积对偶Sylvester共轭和数学算子完备参数解\n",
      "text_b: Cartesian发射机中线性功率放大器的研究\n",
      "cosine_sim: 0.7536823749542236\n",
      "\n",
      "text_a: 多项式矩阵左共轭积对偶Sylvester共轭和数学算子完备参数解\n",
      "text_b: CFRP加固WF型梁侧扭屈曲的几何非线性有限元分析\n",
      "cosine_sim: 0.756926417350769\n",
      "\n",
      "text_a: 多项式矩阵左共轭积对偶Sylvester共轭和数学算子完备参数解\n",
      "text_b: 基于线性CCD自适应成像的光刻机平台调平方法研究\n",
      "cosine_sim: 0.7360574007034302\n",
      "\n",
      "text_a: 多项式矩阵左共轭积对偶Sylvester共轭和数学算子完备参数解\n",
      "text_b: 基于变分贝叶斯理论的图像复原方法研究\n",
      "cosine_sim: 0.7035285234451294\n",
      "\n",
      "text_a: 多项式矩阵左共轭积对偶Sylvester共轭和数学算子完备参数解\n",
      "text_b: 网格资源分配中混合并行蚁群算法方式研究\n",
      "cosine_sim: 0.7051172852516174\n",
      "\n",
      "text_a: 停车信息系统路径诱导最佳路径车位占有率城市交通智能交通\n",
      "text_b: 中心式停车信息系统若干问题的研究\n",
      "cosine_sim: 0.7886505722999573\n",
      "\n",
      "text_a: 停车信息系统路径诱导最佳路径车位占有率城市交通智能交通\n",
      "text_b: 视觉导航区域交通智能车辆（CyberCar）系统研究\n",
      "cosine_sim: 0.8292860388755798\n",
      "\n",
      "text_a: 停车信息系统路径诱导最佳路径车位占有率城市交通智能交通\n",
      "text_b: 需求侧参与输电阻塞管理的模型与算法研究\n",
      "cosine_sim: 0.7751572132110596\n",
      "\n",
      "text_a: 停车信息系统路径诱导最佳路径车位占有率城市交通智能交通\n",
      "text_b: 基于云服务的智能家居系统的研究与设计\n",
      "cosine_sim: 0.7706085443496704\n",
      "\n",
      "text_a: 停车信息系统路径诱导最佳路径车位占有率城市交通智能交通\n",
      "text_b: 环境水质在线监测系统智能主节点的研究与设计\n",
      "cosine_sim: 0.7765445113182068\n",
      "\n",
      "text_a: 停车信息系统路径诱导最佳路径车位占有率城市交通智能交通\n",
      "text_b: 配电网故障自动处理算法的研究及软件开发\n",
      "cosine_sim: 0.7553257346153259\n",
      "\n",
      "text_a: 停车信息系统路径诱导最佳路径车位占有率城市交通智能交通\n",
      "text_b: 基于GeoMedia的高速公路监控系统的研究与开发\n",
      "cosine_sim: 0.7752846479415894\n",
      "\n",
      "text_a: 停车信息系统路径诱导最佳路径车位占有率城市交通智能交通\n",
      "text_b: 基于Java的模块化环境空气质量自动监测系统的研究与设计\n",
      "cosine_sim: 0.7682427167892456\n",
      "\n",
      "text_a: 停车信息系统路径诱导最佳路径车位占有率城市交通智能交通\n",
      "text_b: 边检预检预录系统建设及关键技术研究\n",
      "cosine_sim: 0.7789138555526733\n",
      "\n",
      "text_a: 停车信息系统路径诱导最佳路径车位占有率城市交通智能交通\n",
      "text_b: 基于多技术的路面积水监测预警系统的设计与实现\n",
      "cosine_sim: 0.7860912084579468\n",
      "\n",
      "text_a: 服务企业企业竞争力决定因素提升策略\n",
      "text_b: 服务企业竞争力决定因素与提升策略研究\n",
      "cosine_sim: 0.9679121375083923\n",
      "\n",
      "text_a: 服务企业企业竞争力决定因素提升策略\n",
      "text_b: 提升我国分析仪器产业竞争力的技术创新战略研究\n",
      "cosine_sim: 0.8394899368286133\n",
      "\n",
      "text_a: 服务企业企业竞争力决定因素提升策略\n",
      "text_b: 国有润滑油企业市场开发策略研究\n",
      "cosine_sim: 0.8289150595664978\n",
      "\n",
      "text_a: 服务企业企业竞争力决定因素提升策略\n",
      "text_b: 基于成功要素的企业ERP实施事前评估研究\n",
      "cosine_sim: 0.8313822746276855\n",
      "\n",
      "text_a: 服务企业企业竞争力决定因素提升策略\n",
      "text_b: 环境扫描对企业竞争优势的影响研究--以电子信息行业为例\n",
      "cosine_sim: 0.819771409034729\n",
      "\n",
      "text_a: 服务企业企业竞争力决定因素提升策略\n",
      "text_b: 浦发银行信用卡产品的营销策略研究\n",
      "cosine_sim: 0.8035646677017212\n",
      "\n",
      "text_a: 服务企业企业竞争力决定因素提升策略\n",
      "text_b: 我国出口企业的竞争战略研究\n",
      "cosine_sim: 0.8111944198608398\n",
      "\n",
      "text_a: 服务企业企业竞争力决定因素提升策略\n",
      "text_b: BMP公司供应商绩效指标体系的改进与实施\n",
      "cosine_sim: 0.807074785232544\n",
      "\n",
      "text_a: 服务企业企业竞争力决定因素提升策略\n",
      "text_b: P公司企业管理人员选拔任用体系研究\n",
      "cosine_sim: 0.7709951996803284\n",
      "\n",
      "text_a: 服务企业企业竞争力决定因素提升策略\n",
      "text_b: 高管性别结构、内部制衡与企业技术创新——基于我国创业板上市企业的实证研究\n",
      "cosine_sim: 0.7996144890785217\n",
      "\n",
      "text_a: 数字水印混沌映射版权保护序列密码小波变换\n",
      "text_b: 基于混沌映射的数字水印技术研究\n",
      "cosine_sim: 0.8693466782569885\n",
      "\n",
      "text_a: 数字水印混沌映射版权保护序列密码小波变换\n",
      "text_b: 基于卷积神经网络的图像复杂度研究与应用\n",
      "cosine_sim: 0.7896828651428223\n",
      "\n",
      "text_a: 数字水印混沌映射版权保护序列密码小波变换\n",
      "text_b: 基于图像内容的关键帧检测及VLSI实现\n",
      "cosine_sim: 0.777863621711731\n",
      "\n",
      "text_a: 数字水印混沌映射版权保护序列密码小波变换\n",
      "text_b: 基于局部特征的多光谱与全色图像融合算法研究\n",
      "cosine_sim: 0.7678608894348145\n",
      "\n",
      "text_a: 数字水印混沌映射版权保护序列密码小波变换\n",
      "text_b: 基于嵌入式系统的人脸识别算法研究及其优化\n",
      "cosine_sim: 0.7534335851669312\n",
      "\n",
      "text_a: 数字水印混沌映射版权保护序列密码小波变换\n",
      "text_b: 基于多特征融合和图割模型的遥感影像云检测算法研究\n",
      "cosine_sim: 0.7455176115036011\n",
      "\n",
      "text_a: 数字水印混沌映射版权保护序列密码小波变换\n",
      "text_b: 基于动态符号执行的模糊测试方法研究\n",
      "cosine_sim: 0.7624109983444214\n",
      "\n",
      "text_a: 数字水印混沌映射版权保护序列密码小波变换\n",
      "text_b: 基于交通流增长特性的复杂网络演化建模研究\n",
      "cosine_sim: 0.7177396416664124\n",
      "\n",
      "text_a: 数字水印混沌映射版权保护序列密码小波变换\n",
      "text_b: 基于变分贝叶斯理论的图像复原方法研究\n",
      "cosine_sim: 0.75150465965271\n",
      "\n",
      "text_a: 数字水印混沌映射版权保护序列密码小波变换\n",
      "text_b: 混沌控制和构造延迟混沌系统及应用的研究\n",
      "cosine_sim: 0.7224639058113098\n",
      "\n",
      "text_a: 有限元分析汽车车架焊缝危险部位寿命预测结构强度\n",
      "text_b: 汽车车架焊接结构强度和可靠性分析\n",
      "cosine_sim: 0.9299999475479126\n",
      "\n",
      "text_a: 有限元分析汽车车架焊缝危险部位寿命预测结构强度\n",
      "text_b: 基于天线传感器的FRP-钢结构典型损伤监测方法研究\n",
      "cosine_sim: 0.8614768981933594\n",
      "\n",
      "text_a: 有限元分析汽车车架焊缝危险部位寿命预测结构强度\n",
      "text_b: 有限元强度折减法对抗滑桩加固边坡的优化分析研究\n",
      "cosine_sim: 0.8541433811187744\n",
      "\n",
      "text_a: 有限元分析汽车车架焊缝危险部位寿命预测结构强度\n",
      "text_b: 弹性地基上周期梁板的隔振性能研究\n",
      "cosine_sim: 0.812365710735321\n",
      "\n",
      "text_a: 有限元分析汽车车架焊缝危险部位寿命预测结构强度\n",
      "text_b: SIGMA冷弯薄壁型钢构件畸变屈曲的理论研究\n",
      "cosine_sim: 0.8347386121749878\n",
      "\n",
      "text_a: 有限元分析汽车车架焊缝危险部位寿命预测结构强度\n",
      "text_b: 梁拱组合刚构桥极限承载力分析与研究\n",
      "cosine_sim: 0.8384044170379639\n",
      "\n",
      "text_a: 有限元分析汽车车架焊缝危险部位寿命预测结构强度\n",
      "text_b: CFRP加固WF型梁侧扭屈曲的几何非线性有限元分析\n",
      "cosine_sim: 0.8469037413597107\n",
      "\n",
      "text_a: 有限元分析汽车车架焊缝危险部位寿命预测结构强度\n",
      "text_b: 典型缺陷真型电容式玻璃钢套管电气特征参量测试实验研究\n",
      "cosine_sim: 0.8160465955734253\n",
      "\n",
      "text_a: 有限元分析汽车车架焊缝危险部位寿命预测结构强度\n",
      "text_b: 基于ABB机器人的结构光视觉引导焊缝跟踪技术的研究\n",
      "cosine_sim: 0.8116082549095154\n",
      "\n",
      "text_a: 有限元分析汽车车架焊缝危险部位寿命预测结构强度\n",
      "text_b: 紊流风场中大跨度桥梁非线性气动稳定性研究\n",
      "cosine_sim: 0.829062283039093\n",
      "\n",
      "text_a: 石墨烯导电聚合物复合材料超级电容器\n",
      "text_b: 石墨烯与导电聚合物复合材料的制备以及在超级电容器方面的应用\n",
      "cosine_sim: 0.9174646139144897\n",
      "\n",
      "text_a: 石墨烯导电聚合物复合材料超级电容器\n",
      "text_b: 碳纤维布增强聚酰亚胺基复合材料的制备及其力学和摩擦学性能研究\n",
      "cosine_sim: 0.8341251611709595\n",
      "\n",
      "text_a: 石墨烯导电聚合物复合材料超级电容器\n",
      "text_b: 石墨烯/硅橡胶复合材料的制备及压阻特性研究\n",
      "cosine_sim: 0.8544099926948547\n",
      "\n",
      "text_a: 石墨烯导电聚合物复合材料超级电容器\n",
      "text_b: 功能化碳纳米管在染料敏化太阳能电池对电极中的应用\n",
      "cosine_sim: 0.8149943351745605\n",
      "\n",
      "text_a: 石墨烯导电聚合物复合材料超级电容器\n",
      "text_b: 高介电常数铝阳极复合氧化膜制备技术的研究\n",
      "cosine_sim: 0.840777575969696\n",
      "\n",
      "text_a: 石墨烯导电聚合物复合材料超级电容器\n",
      "text_b: 导电生物可降解聚酯/CNT纤维在神经再生中的研究\n",
      "cosine_sim: 0.78087317943573\n",
      "\n",
      "text_a: 石墨烯导电聚合物复合材料超级电容器\n",
      "text_b: 二维MXene/镍基复合材料制备及其电化学性能研究\n",
      "cosine_sim: 0.824083149433136\n",
      "\n",
      "text_a: 石墨烯导电聚合物复合材料超级电容器\n",
      "text_b: g--C3N4基复合材料的制备及其光催化性能研究\n",
      "cosine_sim: 0.8208093643188477\n",
      "\n",
      "text_a: 石墨烯导电聚合物复合材料超级电容器\n",
      "text_b: 无溶剂厚膜型环氧涂料的制备及其防腐性能的研究\n",
      "cosine_sim: 0.7872056365013123\n",
      "\n",
      "text_a: 石墨烯导电聚合物复合材料超级电容器\n",
      "text_b: 并五苯分子的手性自组装和单层薄膜的结构相变\n",
      "cosine_sim: 0.7821815609931946\n",
      "\n",
      "text_a: 企业管理管理信息系统多层结构框架平台\n",
      "text_b: 基于多层结构的业务框架平台\n",
      "cosine_sim: 0.8617110848426819\n",
      "\n",
      "text_a: 企业管理管理信息系统多层结构框架平台\n",
      "text_b: 基于BPR的管理信息系统开发与应用\n",
      "cosine_sim: 0.8841750621795654\n",
      "\n",
      "text_a: 企业管理管理信息系统多层结构框架平台\n",
      "text_b: 基于BIM的MEP管线综合知识库构建与可视化研究\n",
      "cosine_sim: 0.8084810376167297\n",
      "\n",
      "text_a: 企业管理管理信息系统多层结构框架平台\n",
      "text_b: 基于J2EE的网上书店电子商务应用框架的研究和设计\n",
      "cosine_sim: 0.7906280159950256\n",
      "\n",
      "text_a: 企业管理管理信息系统多层结构框架平台\n",
      "text_b: 基于数字地球平台的中国世界遗产展示平台的设计与实现\n",
      "cosine_sim: 0.7277814745903015\n",
      "\n",
      "text_a: 企业管理管理信息系统多层结构框架平台\n",
      "text_b: 面向组件技术的综合决策支持系统及其商业应用\n",
      "cosine_sim: 0.8238744139671326\n",
      "\n",
      "text_a: 企业管理管理信息系统多层结构框架平台\n",
      "text_b: 在信息管理系统（MIS）平台上进行医学科研项目管理的应用研究\n",
      "cosine_sim: 0.8329774737358093\n",
      "\n",
      "text_a: 企业管理管理信息系统多层结构框架平台\n",
      "text_b: 基于云服务的智能家居系统的研究与设计\n",
      "cosine_sim: 0.7767333388328552\n",
      "\n",
      "text_a: 企业管理管理信息系统多层结构框架平台\n",
      "text_b: 基于PPP模式的W市政道路工程风险管理研究\n",
      "cosine_sim: 0.8238775730133057\n",
      "\n",
      "text_a: 企业管理管理信息系统多层结构框架平台\n",
      "text_b: 基于TD专网移动互联系统及应用的设计与实现\n",
      "cosine_sim: 0.7875232696533203\n",
      "\n",
      "text_a: 纳米CT成像三维图像处理固体氧化物燃料电池多孔材料最优阈值算法边缘检测算法\n",
      "text_b: 纳米CT三维图像处理分析方法及其应用的研究\n",
      "cosine_sim: 0.8609818816184998\n",
      "\n",
      "text_a: 纳米CT成像三维图像处理固体氧化物燃料电池多孔材料最优阈值算法边缘检测算法\n",
      "text_b: 基于线性CCD自适应成像的光刻机平台调平方法研究\n",
      "cosine_sim: 0.850331723690033\n",
      "\n",
      "text_a: 纳米CT成像三维图像处理固体氧化物燃料电池多孔材料最优阈值算法边缘检测算法\n",
      "text_b: 固体中缺陷的超声散射计算与测量技术研究\n",
      "cosine_sim: 0.8514979481697083\n",
      "\n",
      "text_a: 纳米CT成像三维图像处理固体氧化物燃料电池多孔材料最优阈值算法边缘检测算法\n",
      "text_b: 基于多特征融合和图割模型的遥感影像云检测算法研究\n",
      "cosine_sim: 0.8114414811134338\n",
      "\n",
      "text_a: 纳米CT成像三维图像处理固体氧化物燃料电池多孔材料最优阈值算法边缘检测算法\n",
      "text_b: 基于卷积神经网络的图像复杂度研究与应用\n",
      "cosine_sim: 0.8153172135353088\n",
      "\n",
      "text_a: 纳米CT成像三维图像处理固体氧化物燃料电池多孔材料最优阈值算法边缘检测算法\n",
      "text_b: 微纳米结构非线性静动力学分析及其应用\n",
      "cosine_sim: 0.815388560295105\n",
      "\n",
      "text_a: 纳米CT成像三维图像处理固体氧化物燃料电池多孔材料最优阈值算法边缘检测算法\n",
      "text_b: 基于碳纳米管的流体器件设计\n",
      "cosine_sim: 0.8579442501068115\n",
      "\n",
      "text_a: 纳米CT成像三维图像处理固体氧化物燃料电池多孔材料最优阈值算法边缘检测算法\n",
      "text_b: 基于局部特征的多光谱与全色图像融合算法研究\n",
      "cosine_sim: 0.8263983726501465\n",
      "\n",
      "text_a: 纳米CT成像三维图像处理固体氧化物燃料电池多孔材料最优阈值算法边缘检测算法\n",
      "text_b: 基于嵌入式系统的人脸识别算法研究及其优化\n",
      "cosine_sim: 0.8055838942527771\n",
      "\n",
      "text_a: 纳米CT成像三维图像处理固体氧化物燃料电池多孔材料最优阈值算法边缘检测算法\n",
      "text_b: 基于TCAD的VDMOS功率器件仿真研究\n",
      "cosine_sim: 0.8186863660812378\n",
      "\n",
      "text_a: 化学实验教学高师学生问题意识教学策略\n",
      "text_b: 在化学实验教学中培养高师学生的问题意识\n",
      "cosine_sim: 0.9479962587356567\n",
      "\n",
      "text_a: 化学实验教学高师学生问题意识教学策略\n",
      "text_b: 职校计算机专业课有效教学的实践研究\n",
      "cosine_sim: 0.879662036895752\n",
      "\n",
      "text_a: 化学实验教学高师学生问题意识教学策略\n",
      "text_b: 新课程理念下的高中数学分层教学的实践与研究\n",
      "cosine_sim: 0.8497045040130615\n",
      "\n",
      "text_a: 化学实验教学高师学生问题意识教学策略\n",
      "text_b: 信息技术课对提高中学生科学素养的准实验研究\n",
      "cosine_sim: 0.8377701044082642\n",
      "\n",
      "text_a: 化学实验教学高师学生问题意识教学策略\n",
      "text_b: 形象思维理论指导高中物理教学实践的研究\n",
      "cosine_sim: 0.8810827136039734\n",
      "\n",
      "text_a: 化学实验教学高师学生问题意识教学策略\n",
      "text_b: 关于初中生数学归纳能力培养的理论与实践研究\n",
      "cosine_sim: 0.820296585559845\n",
      "\n",
      "text_a: 化学实验教学高师学生问题意识教学策略\n",
      "text_b: 分层教学在生物教学中的初步探索\n",
      "cosine_sim: 0.8521156907081604\n",
      "\n",
      "text_a: 化学实验教学高师学生问题意识教学策略\n",
      "text_b: 课堂教学资源分配的社会学分析--以乌鲁木齐市民、汉学生同班的班级为例\n",
      "cosine_sim: 0.8148094415664673\n",
      "\n",
      "text_a: 化学实验教学高师学生问题意识教学策略\n",
      "text_b: 班级管理对学习动力影响的研究--中小学班级管理中班委会轮值制的效果分析研究\n",
      "cosine_sim: 0.8174724578857422\n",
      "\n",
      "text_a: 化学实验教学高师学生问题意识教学策略\n",
      "text_b: 目标设置在高三物理教学中应用的研究\n",
      "cosine_sim: 0.8291125297546387\n",
      "\n",
      "text_a: 互联网企业互动问答社区产品盈利模式经营策略商业价值\n",
      "text_b: 互联网互动问答社区产品盈利模式选择研究\n",
      "cosine_sim: 0.936973512172699\n",
      "\n",
      "text_a: 互联网企业互动问答社区产品盈利模式经营策略商业价值\n",
      "text_b: 移动互联网时代下网易新闻客户端竞争战略研究\n",
      "cosine_sim: 0.7940401434898376\n",
      "\n",
      "text_a: 互联网企业互动问答社区产品盈利模式经营策略商业价值\n",
      "text_b: 浦发银行信用卡产品的营销策略研究\n",
      "cosine_sim: 0.8403615355491638\n",
      "\n",
      "text_a: 互联网企业互动问答社区产品盈利模式经营策略商业价值\n",
      "text_b: 当前我国电视娱乐节目品牌经营的策略研究\n",
      "cosine_sim: 0.8390094041824341\n",
      "\n",
      "text_a: 互联网企业互动问答社区产品盈利模式经营策略商业价值\n",
      "text_b: 服务企业竞争力决定因素与提升策略研究\n",
      "cosine_sim: 0.8172782063484192\n",
      "\n",
      "text_a: 互联网企业互动问答社区产品盈利模式经营策略商业价值\n",
      "text_b: 基于创新的中国广告产业演化研究\n",
      "cosine_sim: 0.7780814170837402\n",
      "\n",
      "text_a: 互联网企业互动问答社区产品盈利模式经营策略商业价值\n",
      "text_b: 高管性别结构、内部制衡与企业技术创新——基于我国创业板上市企业的实证研究\n",
      "cosine_sim: 0.7984799742698669\n",
      "\n",
      "text_a: 互联网企业互动问答社区产品盈利模式经营策略商业价值\n",
      "text_b: 环境扫描对企业竞争优势的影响研究--以电子信息行业为例\n",
      "cosine_sim: 0.7849807739257812\n",
      "\n",
      "text_a: 互联网企业互动问答社区产品盈利模式经营策略商业价值\n",
      "text_b: 高管团队特征对公司绩效的影响——以我国新三板教育行业公司为例\n",
      "cosine_sim: 0.8026749491691589\n",
      "\n",
      "text_a: 互联网企业互动问答社区产品盈利模式经营策略商业价值\n",
      "text_b: 国有润滑油企业市场开发策略研究\n",
      "cosine_sim: 0.8262608647346497\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "for text_a, text_b_list in text_pairs.items():\n",
    "    text_a_ids = paddle.to_tensor([tokenizer.text_to_ids(text_a)])\n",
    "\n",
    "    for text_b in text_b_list:\n",
    "        text_b_ids = paddle.to_tensor([tokenizer.text_to_ids(text_b)])\n",
    "        print(\"text_a: {}\".format(text_a))\n",
    "        print(\"text_b: {}\".format(text_b))\n",
    "        print(\"cosine_sim: {}\".format(model.get_cos_sim(text_a_ids, text_b_ids).numpy()[0]))\n",
    "        print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecf9c504-a5fc-4704-b787-3b6fd45f65e4",
   "metadata": {},
   "source": [
    "使用VisualDL进行句子向量可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "49430c45-11f6-4908-a495-5404cce37066",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-15T06:59:01.129741Z",
     "iopub.status.busy": "2024-07-15T06:59:01.129369Z",
     "iopub.status.idle": "2024-07-15T06:59:01.132580Z",
     "shell.execute_reply": "2024-07-15T06:59:01.132039Z",
     "shell.execute_reply.started": "2024-07-15T06:59:01.129715Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from visualdl import LogWriter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "eb5d8ba3-0cbc-4644-85e0-df87a9245e4c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-15T06:59:01.671259Z",
     "iopub.status.busy": "2024-07-15T06:59:01.670513Z",
     "iopub.status.idle": "2024-07-15T06:59:01.754450Z",
     "shell.execute_reply": "2024-07-15T06:59:01.754114Z",
     "shell.execute_reply.started": "2024-07-15T06:59:01.671213Z"
    }
   },
   "outputs": [],
   "source": [
    "# 获取句子以及其对应的向量\n",
    "label_list = []\n",
    "embedding_list = []\n",
    "\n",
    "for text_a, text_b_list in text_pairs.items():\n",
    "    text_a_ids = paddle.to_tensor([tokenizer.text_to_ids(text_a)])\n",
    "    embedding_list.append(model(text_a_ids).flatten().numpy())\n",
    "    label_list.append(text_a)\n",
    "\n",
    "    for text_b in text_b_list:\n",
    "        text_b_ids = paddle.to_tensor([tokenizer.text_to_ids(text_b)])\n",
    "        embedding_list.append(model(text_b_ids).flatten().numpy())\n",
    "        label_list.append(text_b)\n",
    "\n",
    "\n",
    "with LogWriter(logdir='./sentence_hidi') as writer:\n",
    "    writer.add_embeddings(tag='test', mat=embedding_list, metadata=label_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2f2dc6b-9e33-478b-bda4-29599276b1f0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-15T07:02:34.833114Z",
     "iopub.status.busy": "2024-07-15T07:02:34.832066Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/arcment/Library/Python/3.9/lib/python/site-packages/pandas/core/arrays/masked.py:59: UserWarning: Pandas requires version '1.3.2' or newer of 'bottleneck' (version '1.2.0' currently installed).\n",
      "  from pandas.core import (\n",
      "\u001b[1;33mVisualDL 2.4.1\u001b[0m\n",
      "Running VisualDL at http://0.0.0.0:8050/ (Press CTRL+C to quit)\n"
     ]
    }
   ],
   "source": [
    "# 启动VisualDL\n",
    "!visualdl --logdir sentence_hidi --host 0.0.0.0 --port 8050"
   ]
  },
  {
   "attachments": {
    "ef8f9c59-35dc-4daf-b6ae-7f6f07e7c761.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "7af851e2-a2ec-4653-8506-7cffd72f506e",
   "metadata": {},
   "source": [
    "![image.png](attachment:ef8f9c59-35dc-4daf-b6ae-7f6f07e7c761.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00227d87-31a2-470a-b55d-833395bd4034",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pnlp",
   "language": "python",
   "name": "pnlp"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
